CN115147159B - A method for selecting a site for a bus charging station with integrated photovoltaic storage and charging - Google Patents

Abstract

The present invention discloses a method for site selection of integrated photovoltaic storage and charging bus charging stations, including: constructing a set of random photovoltaic power generation output power scenarios based on historical weather data and solar irradiance data; determining the bus departure frequency per hour based on bus operation data; constructing an optimization model for site selection of integrated photovoltaic storage and charging bus charging stations; using an L-type decomposition algorithm to solve the optimization model for site selection of integrated photovoltaic storage and charging bus charging stations; and verifying whether the L-type decomposition algorithm meets the termination condition. The present invention aims to minimize the weighted sum of the construction cost of charging facilities, the charging cost of public buses, and the carbon footprint cost. Based on this site selection method, certain social and economic benefits can be achieved for public transportation operators.

Description

A method for selecting a site for a bus charging station with integrated photovoltaic storage and charging

Technical Field

The present invention relates to the technical field of public transportation planning and management, and more specifically to a method for selecting a site for a bus charging station with integrated photovoltaic storage and charging.

Background technique

The integrated photovoltaic storage and charging bus charging station refers to a bus charging station that relies on the integrated photovoltaic storage and charging facilities. The integrated photovoltaic storage and charging facilities include photovoltaic power generation systems, energy storage systems, charging piles and intelligent microgrid management systems. Solar cell modules are generally installed on the roofs of buildings, the roofs of parking sheds and the tops of garages. The function of the energy storage system is to store the electricity produced by solar panels. The charging pile is a three-port photovoltaic grid-connected charging pile, which can realize the dual power supply of the local photovoltaic power generation system and the public distribution network to the vehicle. The function of the intelligent microgrid management system is to monitor, diagnose and manage the integrated photovoltaic storage and charging facilities.

Under normal conditions, integrated photovoltaic, storage and charging facilities have the following advantages: (1) Part of the electricity is generated locally on the demand side through solar panels in a green and low-carbon manner, thereby reducing dependence on the power grid; (2) Electric vehicle batteries and energy storage systems can jointly alleviate the negative impact of large-scale photovoltaic grid connection on the distribution network; (3) The charging costs of bus operators can be reduced by dynamically responding to time-of-use electricity prices. At present, public car charging stations based on integrated photovoltaic, storage and charging facilities have been put into use and have achieved significant social and economic benefits.

However, integrated photovoltaic storage and charging facilities are rarely used in the public transportation sector. One of the main reasons is the lack of a site selection method for integrated photovoltaic storage and charging public transportation charging stations that can quantitatively coordinate social and economic benefits. Therefore, how to provide a scientific and practical site selection method for integrated photovoltaic storage and charging public transportation charging stations is an urgent problem that technicians in this field need to solve.

Summary of the invention

In view of this, the present invention provides a method for selecting a site for a photovoltaic-storage-charging integrated bus charging station, which fully considers the uncertainty of photovoltaic power generation output power. Under the conditions of historical weather data, solar irradiance data and bus operation data, the L-type decomposition algorithm is used to handle the photovoltaic-storage-charging integrated bus charging station site selection problem under large-scale random photovoltaic power generation output power scenarios, thereby automatically generating the optimal photovoltaic-storage-charging integrated bus charging station site selection plan.

In order to achieve the above object, the present invention adopts the following technical solution:

A method for selecting a site for a bus charging station with integrated photovoltaic storage and charging, comprising:

Based on historical weather data and solar irradiance data, a set of random scenarios for photovoltaic power output power is constructed;

Determine the hourly bus departure frequency based on bus operation data;

Based on the random scenario set of bus departure frequency per hour and photovoltaic power generation output power, a photovoltaic storage and charging integrated bus charging station site selection optimization model is constructed;

The L-type decomposition algorithm is used to solve the site selection optimization model of integrated photovoltaic storage and charging bus charging stations. The L-type decomposition algorithm terminates when the termination condition is met.

Preferably, constructing a random scene set of photovoltaic power generation output power specifically includes:

Calculate historical photovoltaic output power based on historical weather data and solar irradiance data;

Based on the historical photovoltaic output power set, a set of random scenarios of photovoltaic power generation output power is constructed.

Preferably, the historical photovoltaic output power calculation formula is:

Wherein, T _cell represents the solar cell temperature, T _NOCT represents the nominal solar cell operating temperature, ρ represents the power temperature coefficient, P _r is the rated power of the photovoltaic power generation module, Ψ _n represents the solar irradiance, and _Ta represents the air temperature.

Preferably, the bus departure frequency per hour is determined by bus GPS data and passenger card swiping data at the departure station.

Preferably, the optimization model for site selection of integrated solar-storage-charging bus charging stations is:

In the objective function, w represents the bus fleet index, j represents the candidate bus charging station index, α _j1 represents the cost of building a photovoltaic storage and charging integrated charging station at j; α _j2 represents the cost of building a common charging station at j; represents the number of days in a year; α ₃ represents the hourly operating cost of the bus; α ₄ represents the purchase price of the bus; x _j1 represents a 0-1 variable, which takes the value of 1 if the candidate charging station j establishes an integrated photovoltaic storage and charging station, otherwise it takes the value of 0; x _j2 represents a 0-1 variable, which takes the value of 1 if the candidate charging station j establishes an ordinary charging station, otherwise it takes the value of 0; y _ii',j,t represents the bus flow from bus line ii' to charging station j at the tth hour; t _i'j and t _ji' represent the travel time of the bus from i' to j and from j to i'respectively; N _w represents the number of vehicles in bus fleet w; E[Q(x ₁ ,g,p)] represents the expectation of the sum of vehicle charging and carbon emission costs.

Preferably, the L-type decomposition algorithm is used to solve the photovoltaic storage and charging integrated bus charging station site selection optimization model, which specifically includes:

The site selection optimization model of integrated photovoltaic storage and charging bus charging stations is decomposed into a main problem and multiple sub-problems;

The L-type decomposition algorithm is used to iteratively solve the main problem and multiple sub-problems.

Preferably, the main question is:

θ≥Cut(S)

θ represents a variable, Cut(S) represents all optimal cuts in the optimal cut set S;

The charging station type constraints are:

Among them, J is the set of candidate bus charging stations;

The charging traffic constraints are:

Wherein, R represents a sufficiently large positive number;

The conservation constraint when filling is:

in, and represents the scheduled departure times of routes ii' and i'i within the tth hour, h′ _ii',t represents the dwelling time of bus flow y _ii',j,t within the tth hour, h′ _i′i,t represents the dwelling time of bus flow y _i'i,j,t within the tth hour, h _ii',j,t represents the total charging time of bus flow y _ii',j,t at charging station j, h _i'i,j,t represents the total charging time of bus flow y _i'i,j,t at charging station j, W is the fleet set, t represents the hour index, and T is the hour set;

This constraint means that the total charging time of bus flow y _ii',j,t at charging station j does not exceed y _ii',j,t hours;

This constraint means that the total charging time of all bus flows at charging station j in the tth period does not exceed c _j ·1, where c _j is the number of charging piles owned by the jth bus charging station;

This constraint means that the total charge of the bus flow y _ii',j,t is less than (1-η _min )E _w y _ii',j,t , where p _gr represents the charging power of the charging pile, η _min represents the minimum SoC allowed for the vehicle, and E _w represents the battery capacity of each vehicle in the bus fleet w;

This constraint defines the total charging amount of bus flow y _ii',j,t , g _ii',j,t represents the total charging amount of bus flow y _ii',j,t at charging station j;

This constraint indicates that the total remaining power of fleet w within the tth hour should not be less than η _t E _w N _w , where η _t represents the minimum SoC allowed for the vehicle per hour, e _ii' and e _i'i represent the energy consumption of the bus from i to i' and from i' to i, respectively, e _ij and e _ji represent the energy consumption of the bus from i to j and j to i, respectively, e _i'j and e _ji' represent the energy consumption of the bus from i' to j and j to i, respectively, g _i'i,j,t' represents the charging amount of the bus flow of route i'i at charging station j during the t'th period, and g _ii',j,t' represents the charging amount of the bus flow of route ii' at charging station j during the t'th period.

This constraint indicates that the total remaining power of fleet w in hour t should not exceed E _w N _w ;

x _j1 ,x _j2 ∈{0,1},j∈J

Preferably, the sub-questions are:

Among them, |K| is the number of elements in the random photovoltaic power generation scenario set K, m represents the month index, D _m represents the number of days in month m, δ _pv represents the carbon footprint cost generated by using the photovoltaic power generation system for each kWh of electricity produced, δ _gr represents the carbon footprint cost generated by the coal-fired power plant for each kWh of electricity produced, λ _t represents the electricity price of the public grid in the tth hour, λ _t ' represents the recovered electricity price of photovoltaic power generation in the tth hour, A _j represents the number of solar panels that can be accommodated by the charging station j, p _mtk represents the photovoltaic output power of the mth month and the tth hour under the kth random scenario, v _mjtk and u _mjtk represent the v _mjt and u _mjt variables under the kth photovoltaic power generation random scenario respectively;

This constraint indicates that the total amount of electricity stored in the energy storage system by the photovoltaic power generation system in the charging station j at month m and time t does not exceed the amount of electricity generated by the photovoltaic system, where v _mjtk represents the total amount of electricity stored in the energy storage system by the photovoltaic power generation system in the charging station j at month m and time t in the kth random scenario, p _mtk represents the unit photovoltaic panel power generation output power at the mth month and the tth hour in the kth random scenario, and M is the set of months;

This constraint indicates that the total amount of electricity transferred from the energy storage system to the vehicle battery by photovoltaic power generation in charging station j at month m and time t does not exceed the total charging demand of the fleet, where u _mjtk represents the total amount of electricity transferred from the energy storage system to the vehicle battery by photovoltaic power generation in charging station j at month m and time t in the kth random scenario;

This constraint indicates that the current total energy storage of the energy storage system does not exceed the energy storage capacity E'_j; v _mjsk indicates the total amount of electricity stored in the energy storage system by the photovoltaic power generation system in the charging station j at month m and time s in the kth random scenario; u _mjsk indicates the total amount of electricity transferred from the energy storage system to the vehicle battery by the photovoltaic power generation in the charging station j at month m and time s in the kth random scenario.

This constraint indicates that the current total energy storage of the energy storage system is greater than or equal to 0;

Preferably, the termination condition is set as the program running time exceeding 10 hours or the number of iterations exceeding 1000 times.

Through the above technical solutions, it can be known that the present invention discloses a method for selecting a site for a bus charging station with integrated photovoltaic storage and charging. First, based on the known historical weather data and solar irradiance data, a set of random photovoltaic power generation output power scenarios is constructed. The historical photovoltaic output power is obtained according to the photovoltaic output power calculation formula. Based on the historical photovoltaic output power set, a clustering method is used to obtain several historical photovoltaic output power modes, and further a random sampling technique is used to extract different output power modes, thereby constructing a set of random photovoltaic power generation output power scenarios. Secondly, the bus departure frequency per hour is determined based on the bus operation data. The departure frequency needs to be satisfied as an important constraint condition in the site selection optimization model for the integrated photovoltaic storage and charging bus charging station. Next, the site selection optimization model for the integrated photovoltaic storage and charging bus charging station is constructed. The purpose of using a mixed integer linear programming model based on bus flow scheduling is twofold: first, compared with the optimization model based on vehicle scheduling, the optimization model based on bus flow scheduling is suitable for instances with large-scale lines; second, the optimization model based on bus flow scheduling clearly describes the key modeling elements such as fleet charging, scheduling, energy consumption, and current battery power. On this basis, the L-type decomposition algorithm is used to solve the site selection optimization model of integrated photovoltaic storage and charging bus charging stations.

Compared with the prior art, the present invention jointly optimizes the site selection of charging stations, vehicle flow scheduling, vehicle flow charging and energy scheduling; uses a set of random photovoltaic power generation output power scenarios to ensure that the optimization model provides the expected minimum target value; adopts an L-type decomposition algorithm to ensure the solvability of the optimization model in the face of large-scale complex networks, and provides technical methods for planning and management of integrated photovoltaic storage and charging facilities for the promotion of public transportation.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

Figure 1 is a flow chart of the site selection method for integrated photovoltaic storage and charging bus charging stations provided by the present invention.

Figure 2 is a schematic diagram of the structure and function of the integrated photovoltaic storage and charging facility provided by the present invention.

FIG. 3 is a geographic information map of the bus network according to an embodiment of the present invention.

FIG. 4 is an optimal site selection solution for an integrated photovoltaic storage and charging station provided in an embodiment of the present invention.

Detailed ways

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

As shown in Figure 1, an embodiment of the present invention discloses a method for selecting a site for a photovoltaic, storage and charging integrated bus charging station. The present invention introduces photovoltaic, storage and charging integrated charging facilities into the bus system. Each candidate bus charging station can choose not to establish a charging station, establish a common charging station, or establish a photovoltaic, storage and charging integrated charging station. The structure and function of the photovoltaic, storage and charging integrated charging facility are explained below in conjunction with Figure 2. The charging pile is connected to both the public power grid and the energy storage system. Therefore, the bus can receive electricity generated by both the photovoltaic power generation system and the public power grid. The electricity generated by the photovoltaic power generation system can be sold directly online or stored in the energy storage system. The function of the intelligent microgrid management system is to monitor, diagnose, and manage photovoltaic, storage and charging integrated charging facilities. It is the hub of the local photovoltaic, storage and charging integrated charging facilities.

The specific process steps of the site selection method for integrated photovoltaic storage and charging bus charging stations are as follows:

S1. Based on historical weather data and solar irradiance data, a set of random scenarios for photovoltaic power generation output power is constructed. When the weather data and solar irradiance data are known, the calculation formula for photovoltaic power generation output power _Pn is as follows:

Wherein, T _cell represents the solar cell temperature, T _NOCT represents the nominal solar cell operating temperature, ρ represents the power temperature coefficient, P _r is the rated power of the photovoltaic power generation module, Ψ _n represents the solar irradiance, and _Ta represents the air temperature.

Based on the historical photovoltaic output power set, the K-means clustering method is used to obtain several historical photovoltaic output power modes, and further random sampling technology is used to extract different output power modes, thereby constructing a random scenario set of photovoltaic power generation output power. A random scenario is represented by a 12-by-24 matrix, the rows of which represent months, the columns represent hours, and the elements represent the unit photovoltaic panel power generation output power at the mth month and the tth hour. Such a matrix set constitutes a random scenario set of photovoltaic power generation output power. Let p _mtk represent the unit photovoltaic panel power generation output power at the mth month and the tth hour in the kth scenario.

S2. Determine the bus departure frequency per hour based on the bus operation data. The bus departure frequency per hour is determined by the bus GPS data and the passenger card swiping data at the departure station. The bus GPS data records the ID, location, speed, acceleration, time, and route of each bus. The passenger card swiping data records the passenger's boarding time, station, route, and other information. By matching the bus GPS data with the passenger card swiping data, the departure time of each bus on each bus route can be obtained, and then the bus departure frequency per hour can be obtained.

S3. Construct an optimization model for the site selection of bus charging stations with integrated photovoltaic storage and charging. Table 1 lists the parameters and variables of the optimization model.

The optimization model is expressed as follows:

In the objective function, m represents the month index, M is the month set; t represents the hour index, T is the hour set; ii' represents the bus line index, L is the line set; w represents the bus fleet index, W is the fleet set; j represents the candidate bus charging station index, J is the candidate bus charging station set; α _j1 represents the cost of building a photovoltaic storage and charging integrated charging station at j (yuan/year); α _j2 represents the cost of building a common charging station at j (yuan/year); represents the number of days in a year; α ₃ represents the hourly operating cost of the bus (yuan/hour); α ₄ represents the purchase price of the bus (yuan/year); x _j1 represents a 0-1 variable. If the candidate charging station j establishes an integrated photovoltaic storage and charging station, then the value is 1, otherwise it is 0; x _j2 represents a 0-1 variable. If the candidate charging station j establishes an ordinary charging station, then the value is 1, otherwise it is 0; y _ii',j,t represents the bus flow from bus line ii' to charging station j at the tth hour; t _i'j and t _ji' represent the travel time of the bus from i' to j and from j to i'respectively; N _w represents the number of vehicles in bus fleet w; E[Q(x ₁ ,g,p)] represents the expectation of the sum of vehicle charging and carbon emission costs.

This constraint is a charging station type constraint.

This constraint is a charging vehicle flow constraint. Wherein, R represents a sufficiently large positive number.

This constraint is a full-time conservation constraint. and represents the scheduled number of departures of routes ii' and i'i in the tth hour. _{h i} '_i',t represents the dwelling time of bus flow y _ii',j,t in the tth hour. h′ _i′i,t represents the dwelling time of bus flow y _i'i,j,t in the tth hour. _{h ii',j,t} represents the total charging time of bus flow y _ii',j,t at charging station j, and h _i'i,j,t represents the total charging time of bus flow y _i'i,j,t at charging station j.

This constraint indicates that the total charging time of bus flow y _ii',j,t at charging station j shall not exceed y _ii',j,t hours.

This constraint means that the total charging time of all bus flows at charging station j in the tth period does not exceed c _j · 1. Where c _j is the number of charging piles owned by the jth bus charging station.

This constraint indicates that the total charge of the bus flow y _ii',j,t is less than (1-η _min )E _w y _ii',j,t . Where p _gr represents the charging power of the charging pile. η _min represents the minimum SoC allowed by the vehicle (usually 20%). E _w represents the battery capacity of each vehicle in the bus fleet w.

This constraint defines the total charge amount of bus flow y _ii',j,t . _{g ii',j,t} represents the total charge amount of bus flow y _ii',j,t at charging station j.

This constraint indicates that the total remaining power of the fleet w in the tth hour should not be less than η _t E _w N _w . Where η _t represents the minimum SoC allowed for the vehicle per hour. _{e ii'} and e _i'i represent the energy consumption of the bus from i to i' and from i' to i, respectively. e _ij and e _ji represent the energy consumption of the bus from i to j and j to i, respectively. e _i'j and e _ji' represent the energy consumption of the bus from i' to j and j to i, respectively. g _i'i,j,t' represents the amount of charge of the bus flow of route i'i at charging station j during the t' period. g _ii',j,t' represents the amount of charge of the bus flow of route ii' at charging station j during the t' period.

This constraint states that the total remaining power of fleet w should not exceed E _w N _w in the tth hour.

x _j1 ,x _j2 ∈{0,1},j∈J

The above constraints define the value range of the decision variables.

Where D _m represents the number of days in month m. δ _pv represents the carbon footprint cost of using the photovoltaic power generation system to produce 1 kWh of electricity. δ _gr represents the carbon footprint cost of using the coal-fired power plant to produce 1 kWh of electricity. λ _t represents the electricity price of the public grid at the tth hour. λ _t ' represents the price of electricity recovered from photovoltaic power generation at the tth hour. A _j represents the number of panels that charging station j can accommodate. p _mtk represents the photovoltaic output power at the tth hour of the mth month under the kth random scenario, v _mjtk and u _mjtk represent the v _mjt and u _mjt variables under the kth photovoltaic power generation random scenario, respectively, u _mjt represents the total amount of electricity transferred from the energy storage system to the car battery by photovoltaic power generation in charging station j at month m and time t (kWh), and v _mjt represents the total amount of electricity stored in the energy storage system by the photovoltaic power generation system in charging station j at month m and time t (kWh).

This constraint indicates that the total amount of electricity stored in the energy storage system by the photovoltaic power generation system in the charging station j at month m and time t does not exceed the amount of electricity generated by the photovoltaic system. Among them, v _mjtk represents the total amount of electricity stored in the energy storage system by the photovoltaic power generation system in the charging station j at month m and time t in the kth random scenario, p _mtk represents the unit photovoltaic panel power generation output power at the mth month and the tth hour in the kth random scenario, and M is the set of months;

This constraint indicates that the total amount of electricity transferred from the energy storage system to the vehicle battery by photovoltaic power generation in charging station j at month m and time t does not exceed the total charging demand of the fleet. Among them, u _mjtk represents the total amount of electricity transferred from the energy storage system to the vehicle battery by photovoltaic power generation in charging station j at month m and time t in the kth random scenario.

This constraint indicates that the current total energy storage of the energy storage system does not exceed the energy storage capacity E' _j .

This constraint indicates that the current total energy storage of the energy storage system is greater than or equal to 0.

The above two constraints define the value range of the decision variables.

The site selection optimization model of integrated photovoltaic storage and charging bus charging stations is a mixed integer linear programming model based on bus flow scheduling. The optimization model provides decisions such as charging station site selection, traffic flow scheduling, traffic flow charging, and energy scheduling. The objective function of the optimization model is to minimize the sum of charging costs, charging station construction costs, vehicle operating costs, and carbon footprint costs. Constraints include traffic organization constraints, fleet current SoC constraints, site selection constraints, and technical constraints related to photovoltaic power generation and vehicle charging.

S4. Use the L-type decomposition algorithm to solve the optimization model of the location selection of bus charging stations with integrated photovoltaic storage and charging. The specific solution steps are as follows:

Step 1: Let θ = -∞, let S be an empty set, S represents the set storing the optimal cut, and θ is an artificial variable in the main problem of the algorithm;

Step 2: Solve the main problem using a commercial solver;

Step 3: Solve the subproblems using commercial solvers;

Step 4: If Among them, θ ^* , g ^* is the optimal solution corresponding to the main problem. Terminate the algorithm; otherwise, add the optimal cut to the main problem, update S, and return to the second step.

Among them, the optimal cut is expressed as follows:

Here, |K| is the number of elements in the random photovoltaic power generation scenario set K. is the dual variable of the subproblem. Let Cut(S) represent all the optimal cuts in the optimal cut set S.

The main problem is expressed as follows:

θ≥Cut(S)

x _j1 ,x _j2 ∈{0,1},j∈J

The sub-problems are expressed as follows:

Among them, v _mjtk and u _mjtk represent the v _mjt and u _mjt variables under the kth random scenario of photovoltaic power generation, respectively.

S5. Check whether the L-type decomposition algorithm meets the termination condition. When the program running time exceeds 10 hours or the number of iterations exceeds 1000, the L-type decomposition algorithm automatically terminates. Therefore, the L-type decomposition algorithm can terminate if it meets any of the following conditions: (1) the program running time exceeds 10 hours or the number of iterations exceeds 1000; (2)

The embodiments of the present invention are as follows. As shown in Figure 2, 34 (bidirectional) real bus routes in City A are shown, as well as 20 bus starting and ending stations and 15 candidate bus charging stations. The bus operation period selected in the embodiment is 5:00-23:00, and the bus maintenance and rest period is 23:00-5:00 the next day. The bus operation data, historical weather data and solar irradiance data cover all months in 2019. The specific parameters of the optimization model are known information of the present invention and will not be further described here.

S1. Based on historical weather data and solar irradiance data, a random photovoltaic power generation output power scenario set is constructed. The photovoltaic power generation panel model used in the embodiment is Sun Power E20-327. The specific parameters are shown in Table 1.

Table 1: Sun Power E20-327 photovoltaic panel parameters

The historical photovoltaic panel output power can be calculated according to the parameters in Table 1. Based on the historical photovoltaic output power set, the K-means clustering method is used to obtain several historical photovoltaic output power patterns, and the results are shown in Table 2.

Table 2: K-means clustering results

Random sampling technology is further used to extract different output power modes, thereby constructing a set of random scenarios for photovoltaic power generation output power. A random scenario is represented by a 12-by-24 matrix, where the rows represent months, the columns represent hours, and the elements represent the output power of photovoltaic panels per unit at the mth month and the tth hour. Such a set of matrices constitutes a set of random scenarios for photovoltaic power generation output power. Let p _mtk represent the output power of photovoltaic panels per unit at the mth month and the tth hour under the kth scenario.

S2. Determine the bus departure frequency per hour based on bus operation data. By matching bus GPS data with passenger card swiping data through SQ Server, the departure time of each bus on each bus line can be obtained, and then the bus departure frequency per hour can be obtained.

S3. Constructing a photovoltaic storage and charging integrated bus charging station site selection optimization model. Based on the embodiment, the parameter values of the model in Table 1 are substituted into the optimization model to form an example of the optimization model.

S4. Use the L-type decomposition algorithm to solve the "solar energy storage and charging" integrated bus charging station site selection optimization model. Follow the specific solution steps of the L-type decomposition algorithm to solve an example of the optimization model, use MATLAB to write a program to execute the L-type decomposition algorithm, and call the Gurobi solver to solve the main problem and sub-problems.

S5. Check whether the L-type decomposition algorithm meets the termination condition. FIG4 shows the optimal site selection scheme for the photovoltaic storage and charging integrated charging station provided by an embodiment of the present invention.

In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims (7)

1. A method for selecting a site for a bus charging station with integrated photovoltaic storage and charging, comprising: Based on historical weather data and solar irradiance data, a set of random scenarios for photovoltaic power output power is constructed; Determine the hourly bus departure frequency based on bus operation data; Based on the random scenario set of bus departure frequency per hour and photovoltaic power generation output power, a photovoltaic storage and charging integrated bus charging station site selection optimization model is constructed; The L-type decomposition algorithm is used to solve the optimization model of location selection of bus charging stations with integrated photovoltaic storage and charging. The L-type decomposition algorithm terminates when the termination condition is met; The optimization model for site selection of integrated photovoltaic storage and charging bus charging stations is as follows: In the objective function, w represents the bus fleet index, j represents the candidate bus charging station index, α _j1 represents the cost of building a photovoltaic storage and charging integrated charging station at j; α _j2 represents the cost of building a common charging station at j; represents the number of days in a year; α ₃ represents the hourly operating cost of the bus; α ₄ represents the purchase price of the bus; x _j1 represents a 0-1 variable, if the candidate charging station j builds a photovoltaic storage and charging integrated charging station, then the value is 1, otherwise it is 0; x _j2 represents a 0-1 variable, if the candidate charging station j builds a common charging station, then the value is 1, otherwise it is 0; y _ii',j,t represents the bus flow from bus line ii' to charging station j at the tth hour; t _i'j and t _ji' represent the travel time of the bus from i' to j and j to i'respectively; N _w represents the number of vehicles in bus fleet w; E[Q(x ₁ ,g,p)] represents the expectation of the sum of vehicle charging and carbon emission costs; The L-type decomposition algorithm is used to solve the optimization model of the location selection of integrated photovoltaic storage and charging bus charging stations, which includes: The site selection optimization model of integrated photovoltaic storage and charging bus charging stations is decomposed into a main problem and multiple sub-problems; The L-type decomposition algorithm is used to iteratively solve the main problem and multiple sub-problems. 2. A method for selecting a site for a bus charging station with integrated photovoltaic storage and charging according to claim 1, characterized in that constructing a random scene set of photovoltaic power generation output power specifically comprises: Calculate historical photovoltaic output power based on historical weather data and solar irradiance data; Based on the historical photovoltaic output power set, a set of random scenarios of photovoltaic power generation output power is constructed. 3. A method for selecting a site for a bus charging station with integrated photovoltaic storage and charging according to claim 2, characterized in that the historical photovoltaic output power _Pn is calculated as follows: Wherein, T _cell represents the solar cell temperature, T _NOCT represents the nominal solar cell operating temperature, ρ represents the power temperature coefficient, P _r is the rated power of the photovoltaic power generation module, Ψ _n represents the solar irradiance, and _Ta represents the air temperature. 4. A method for selecting a site for a bus charging station with integrated photovoltaic storage and charging according to claim 1, characterized in that the bus departure frequency per hour is determined by the bus GPS data and passenger card swiping data at the departure station. 5. According to the method for selecting a site for a bus charging station with integrated photovoltaic storage and charging as claimed in claim 1, the main problem is: θ≥Cut(S) θ represents a variable, Cut(S) represents all optimal cuts in the optimal cut set S; The charging station type constraints are: Among them, J is the set of candidate bus charging stations; The charging traffic constraints are: Wherein, R represents a sufficiently large positive number; The charging hour conservation constraint is: in, and represents the scheduled departure times of routes ii' and i'i within the tth hour, h'_ii',t represents the dwelling time of bus flow y _ii',j,t within the tth hour, h'_i'i,t represents the dwelling time of bus flow y _i'i,j,t within the tth hour, h _ii',j,t represents the total charging time of bus flow y _ii',j,t at charging station j, h _i'i,j,t represents the total charging time of bus flow y _i'i,j,t at charging station j, W is the fleet set, t represents the hour index, and T is the hour set; This constraint means that the total charging time of bus flow y _ii',j,t at charging station j does not exceed y _ii',j,t hours; This constraint means that the total charging time of all bus flows at charging station j in the tth period does not exceed c _j ·1, where c _j is the number of charging piles owned by the jth bus charging station; This constraint means that the total charge of the bus flow y _ii',j,t is less than (1-η _min )E _w y _ii',j,t , where p _gr represents the charging power of the charging pile, η _min represents the minimum SoC allowed for the vehicle, and E _w represents the battery capacity of each vehicle in the bus fleet w; This constraint defines the total charging amount of bus flow y _ii',j,t , g _ii',j,t represents the total charging amount of bus flow y _ii',j,t at charging station j; This constraint indicates that the total remaining power of the fleet w within the tth hour should not be less than η _t E _w N _w , where η _t represents the minimum SoC allowed for the vehicle per hour, e _ii' and e _i'i represent the energy consumption of the bus from i to i' and from i' to i, respectively, e _ij and e _ji represent the energy consumption of the bus from i to j and j to i, respectively, e _i'j and e _ji' represent the energy consumption of the bus from i' to j and j to i', respectively, g _i'i,j,t' represents the charging amount of the bus flow of route i'i at the charging station j during the t'th period, and g _ii',j,t' represents the charging amount of the bus flow of route ii' at the charging station j during the t'th period; This constraint indicates that the total remaining power of fleet w in hour t should not exceed E _w N _w ; x _j1 ,x _j2 ∈{0,1},j∈J 6. A method for selecting a location for a bus charging station with integrated photovoltaic storage and charging according to claim 5, characterized in that the sub-problems are: Among them, |K| is the number of elements in the random photovoltaic power generation scenario set K, m represents the month index, D _m represents the number of days in month m, δ _pv represents the carbon footprint cost generated by using the photovoltaic power generation system for each kWh of electricity produced, δ _gr represents the carbon footprint cost generated by the coal-fired power plant for each kWh of electricity produced, λ _t represents the electricity price of the public grid in the tth hour, λ _t ' represents the recovered electricity price of photovoltaic power generation in the tth hour, A _j represents the number of solar panels that can be accommodated by the charging station j, p _mtk represents the photovoltaic output power of the mth month and the tth hour under the kth random scenario, v _mjtk and u _mjtk represent the v _mjt and u _mjt variables under the kth photovoltaic power generation random scenario respectively; This constraint indicates that the total amount of electricity stored in the energy storage system by the photovoltaic power generation system in the charging station j at month m and time t does not exceed the amount of electricity generated by the photovoltaic system, where v _mjtk represents the total amount of electricity stored in the energy storage system by the photovoltaic power generation system in the charging station j at month m and time t in the kth random scenario, p _mtk represents the unit photovoltaic panel power generation output power at the mth month and the tth hour in the kth random scenario, and M is the set of months; This constraint indicates that the total amount of electricity transferred from the energy storage system to the vehicle battery by photovoltaic power generation in charging station j at month m and time t does not exceed the total charging demand of the fleet, where u _mjtk represents the total amount of electricity transferred from the energy storage system to the vehicle battery by photovoltaic power generation in charging station j at month m and time t in the kth random scenario; This constraint indicates that the total current energy storage of the energy storage system does not exceed the energy storage capacity E' _j , v _mjsk indicates the total amount of electricity stored in the energy storage system by the photovoltaic power generation system in the charging station j at month m and time s in the kth random scenario, and u _mjsk indicates the total amount of electricity transferred from the photovoltaic power generation in the charging station j from the energy storage system to the vehicle battery in the kth random scenario at month m and time s; This constraint indicates that the current total energy storage of the energy storage system is greater than or equal to 0; 7. A method for selecting a bus charging station with integrated photovoltaic storage and charging according to claim 1, characterized in that the termination condition is set as the program running time exceeds 10 hours or the number of iterations exceeds 1000 times.